This study introduces a data-driven modeling strategy for smart grid power quality (PQ) coupling assessment based on time series pattern matching to quantify the influence of single and integrated disturbance among nodes in different pollution patterns. Periodic and random PQ patterns are constructed by using multidimensional frequency-domain decomposition for all disturbances. A multidimensional piecewise linear representation based on local extreme points is proposed to extract the patterns features of single and integrated disturbance in consideration of disturbance variation trend and severity. A feature distance of pattern (FDP) is developed to implement pattern matching on univariate PQ time series (UPQTS) and multivariate PQ time series (MPQTS) to quantify the influence of single and integrated disturbance among nodes in the pollution patterns. Case studies on a 14-bus distribution system are performed and analyzed; the accuracy and applicability of the FDP in the smart grid PQ coupling assessment are verified by comparing with other time series pattern matching methods.
Harmonic pollution sources in microgrids have the characteristics of high penetration and decentralization, as well as forming a full network. Local harmonic mitigation is a traditional harmonic mitigation method, which has the disadvantages of complexity and costly operation. Based on the idea of the decentralized autonomy of power quality, this paper establishes a comprehensive optimization model of the active power and harmonic mitigation capacities of grid-connected inverters based on two-layer optimization and realizes harmonic mitigation. Firstly, based on the harmonic sensitivity analysis, the calculation method of harmonic mitigation capacity is given. Secondly, a two-layer model of harmonic mitigation optimization is established. The upper-layer optimization model takes the minimum operation cost of the microgrid as the objective and the active power reduction in the multi-functional grid-connected inverter (MFGCI) as the optimization variable. The lower-layer optimization model offers the be st harmonic mitigation effect as the objective and the harmonic current compensation as the optimization variable. According to the relationship between the total remaining capacity of MFGCI and the capacity required for harmonic mitigation, there are three different objective functions in the lower-layer optimization model. Then, the model solving steps are provided. Finally, an example shows that the proposed optimization model can achieve harmonic mitigation at different times. Compared with the case without active power optimization, the operation cost of the whole system can be reduced by up to 14.6%, while ensuring the harmonic mitigation effect. The proposed method has the advantages of a harmonic mitigation effect and economical system operation.
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